CeO2 is widely used in the field of chemical–mechanical polishing for integrated circuits. Morphology, particle size, crystallinity, and Ce3+ concentration are crucial factors that affect polishing ...performance. In this study, we successfully synthesized two novel triangular CeO2 abrasives with similar particle sizes (600 nm) but different morphologies and Ce3+ concentrations using a microwave-assisted hydrothermal method with high-concentration raw materials, and no surfactants or template agents were added. It is generally believed that CeO2 with a higher Ce3+ concentration leads to better polishing performance. However, the results of polishing indicate that CeO2 synthesized at 200 °C, despite its lower Ce3+ concentration, demonstrates outstanding polishing performance, achieving a polishing rate of 324 nm/min, and the Sa of Si wafers decreased by 3.6% after polishing. This suggests that, under similar particle size conditions, the morphology of CeO2 plays a dominant role in the mechanical effects during the polishing process. Additionally, compared to commercial polishing slurries, the synthesized samples demonstrated better polishing performance. This indicates that, in CMP, the pursuit of smaller spherical abrasives may not be necessary. Instead, the appropriate shape and particle size can better balance the material removal rate and surface roughness.
Pollen allergens, widely present in the atmosphere, are the main cause of seasonal respiratory diseases that affect millions of people worldwide. Although previous studies have reported that nitrogen ...dioxide (NO2) and ozone (O3) promote pollen allergy, the specific biological processes and underlying mechanisms remain less understood. In this study, Platanus pollen grains were exposed to gaseous pollutants (NO2 and O3). We employed environmental electron microscopy, flow cytometry, western blot assay, enzyme-linked immunoassay, ultraviolet absorption spectrometry, circular dichroism, and protein mass spectrometry to characterise the subpollen particles (SPPs) released from pollen grains. Furthermore, we determined the immunogenicity and pathogenicity induced by Platanus pollen allergen a 3 (Pla a 3). Our results demonstrated that NO2 and O3 could damage the pollen cell membranes in SPPs and increase the amount of Pla a 3 allergen released into the atmosphere. Additionally, NO2 and O3 altered the structure of Pla a3 protein through nitrification and oxidation, which not only enhanced the immunogenicity of allergens but also increased the stability of the protein. In vivo analysis using an animal model indicated that NO2 and O3 greatly aggravated pollen-induced pneumonia. Thus, our study provides guidance for the prevention of pollen allergic diseases.
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•Allergenic protein (including Pla a3) found in subpollen particles (SPPs).•NO2 and O3 could damage the pollen cell membrane.•NO2 and O3 could change the structure of Pla a3 protein and aggravate pollen allergic reaction.
Due to the COVID-19 pandemic in early 2020, large-scale industrial production has been stagnant and reduced, the urban air quality has been greatly improved. It provided an excellent opportunity to ...explore the effects of air pollutants on the sensitization of pollen allergen proteins in the environment.
Platanus
pollen grains sampled in the spring of 2019 and 2020 were used for detailed characterization and analysis. Scanning electron microscopy, Fourier transform infrared, X-ray spectroscopy (XPS), trypan blue staining, and western blot analysis were employed to characterize
Platanus
pollen protein released from pollen grains. Our data showed that the viability of the pollen grains in 2019 was lower compared that in 2020, and the pollen grains collected in 2019 had a higher absorption peak of protein functional groups. The XPS spectra assay result demonstrated that the binding energy of the high-resolution components had not variation on the surface of pollen grains, but relative content of nitrogen and peptide chain in the pollen grains sampled in 2019 were higher than in 2020. These results suggested that more protein in the pollen grains was released onto the surface of pollen grains. In addition, western blot assay showed that the expression of Pla a3 protein in pollen grains sampled in 2019 was significantly higher than that in 2020, revealing that air pollutants could enhance the expression of Pla a3 proteins in
Platanus
pollen.
Workflow of the ML/SISSO-accelerated MSI degree evaluation on TM/In2O3-ZrO2 substrate.
The central focus of this study is to explore the metal-support interactions (MSI) between the metal species and ...the substrate in TM/In2O3-ZrO2 catalysts, extracting data from theoretical calculations and employing machine learning (ML) and the SISSO methods to characterize the degree of MSI. Firstly, we select the CO2 hydrogenation to methanol reaction as our research target. Subsequently, we conduct the density functional theory (DFT) calculations to investigate the formate pathway in defective In2O3-ZrO2 models loaded with Cu, Ni, and Pd, extracting the activation energies of each respective rate-determining step as indicators for evaluating the reaction. Then, from an experimental and characterization perspective, we investigate the CO2 conversion, methanol yield, and product selectivity at different temperatures, providing experimental evidence for our prior theoretical results. By combining computational and experimental data, we find a close correlation between the degree of MSI and the activation energy of the rate-determining steps, thus selecting activation energy as the target for prediction. Among a series of theoretical data, we select applicable thermodynamic features as potential descriptors and use scaling relations to identify two descriptors with high linear correlation, serving as the fundamental inputs for the SISSO method. Additionally, we attempt to incorporate all descriptors into ML methods for measurement and find that neural network (NN) method demonstrates the best predictive accuracy. In summary, this work provides a comprehensive approach spanning theoretical calculations, experimental validation and data analysis for MSI investigation, offering valuable analytical methods for CO2 hydrogenation research.
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•Methanol synthesis by CO2 hydrogenation was studied by a multifaceted approach.•Ni-modified In2O3-ZrO2 catalyst presented thermodynamic and kinetic superiority.•Two improvement strategies were proposed based on SISSO and ML methods.•An evaluation template for MSI degree between TM and carrier was established.
Amidst the escalating concerns surrounding energy and environmental issues, the hydrotreatment of emitted CO2 and conversion into C1 products, such as methanol, have emerged as a pivotal strategy for effective mitigation. Previous investigations have identified the potential of indium-zirconium oxide catalysts doped with transition metals (TMs) for methanol synthesis. However, a comprehensive understanding of the impact of different TM loadings on catalytic performance, as well as a quantitative elucidation of the degree of metal-support interaction (MSI) between TM and substrate, necessitates further exploration. A multifaceted approach covering density functional theory (DFT) calculation, experimental validation and machine learning (ML) analysis is employed in this study to elucidate the underlying principles of MSI influence on hydrogenation, culminating in two general models for predicting the activation energy representing the degree of MSI. Consistent findings from theoretical calculations and experimental results demonstrate that, in comparison to Cu and Pd, Ni doped In2O3-ZrO2 catalyst shows superior performance both in thermodynamics and kinetics. This enhancement is attributed to the strong degree of MSI relationship between thermodynamically stable Ni and substrate, consequently leading to the enhancement in reducibility. Moreover, a three-layer framework is constructed to predict the activation energy in the formate pathway, which facilitates the evaluation of MSI degree in TM-loaded catalysts. To sum up, this work provides guidance for CO2 hydrogenation studies and quantitative analysis of MSI, contributing to a heightened understanding of the interaction mechanisms in analogous research systems.
•Methanol synthesis by CO2 hydrogenation was studied using DFT and ML methods.•Spillover facilitated the activation of hydrogenation in relative and absolute terms.•SISSO promotes the prediction ...performance of machine learning models.•Four-level descriptor screening scheme is proposed for reference.
With the increasing attention in environmental issues caused by CO2 emissions, methanol conversion by CO2 hydrogenation is an effective strategy to solve this existing energy dilemma. The rationale behind hydrogen spillover on methanol synthesis is unraveled via density functional theory (DFT) calculations in this work, furthermore, the activation energy of hydrogen transfer process as affected by spillover is also summarized in a general paradigm for facilitating the understanding of hydrogenation characteristics. The results demonstrate that the spillover strategy significantly facilitates the hydrogenation reaction by supplying available hydrogen adatoms. This effect is particularly pronounced during the stage when OH is formed directly at the substrate site and combines with H to produce H2O, leading to a substantial reduction in activation energy from the initial 3.74 eV to 0.78 eV. In addition, a comprehensive predictive model for the kinetic characteristics of hydrogen spillover process is established based on the machine learning algorithm and SISSO guidance. By employing the combined approach of SISSO and neural network, we have achieved a stable prediction performance for activation energy with R2 = 0.99 and RMSE = 0.07 eV. The variable of ChgFSAu is identified as the most representative factor in describing the activation energy, demonstrating a correlation coefficient of -0.60. The extended multidimensional expression of DistAu further highlights its close connection to activation energy, achieving an RMSE value of 0.41 eV. To sum up, this work elucidates the possible thoughts of catalyst design with spillover effect and gives reference for the description screening towards the chemical reactions similar to hydrogen spillover.
This figure summarizes the descriptor screening strategy in predicting the activation energy of hydrogen transfer reactions on modified In2O3 surfaces. It comprises four levels, each building upon the previous to enhance the model simplicity while ensuring accuracy. The first level represents the initial descriptors selected from energy, structure, and charge features, such as DistReactant, MigH, ChgFSAu and Δ∅Au + Reactant − Sub that exhibit high linear correlation with activation energy. The second level consists of comprehensive descriptors derived from the linear regression integration of the first level descriptors using SISSO method. This descriptor, denoted as SISSO8, exhibits the highest linear correlation with activation energy. The third level includes descriptors based on SISSO regression integration for ChgIS−FSReactant, ChgISReactant, ChgIS−FSAu, ChgISAu and ChgFSAu. This combination displays the highest linear correlation among single attribute descriptors, with an RMSE value of 0.28 eV. The fourth level involves descriptors established based on the SISSO regression equation for DistAu. This method enables effective prediction of activation energy using only the single variable, with a RMSE value of 0.41 eV. Display omitted
•The promoting effect of atom doping on CO2 methanolization over Cu/ZnO based catalyst is studied using DFT method.•Cu-ZnO heterostructure improves the fromate pathway of CO2 hydrogenation by MSI ...(metal-support interaction).•The scaling relationships between adsorption energies with the correlation coefficients R2≈0.8 are extracted.•The performance of various metal promoters is ranked as Al > Ga > Mg, Pt > Pd > Au.
The usage of CO2, a vital carbon source, is of great application value in carbon neutrality. Hydrogenation is one of the most promising approaches to convert the CO2 into high-value chemicals like methanol. The development of the hydrogenation of CO2 mainly lies in the design of safe and efficient catalysts. Focusing on the mechanism and the interface effects, the hydrogenation of CO2 to methanol over Cu/ZnO-based catalyst was investigated in this work. To study the enhancing effect of metal promoters, atomic doping was simulated on Cu/ZnO-X(Al,Mg,Ga,Pt,Pd,Au) catalyst. Based on the designed atomic doped mode, density functional theory (DFT) calculation was conducted to analyze the adsorption of intermediates, thermodynamic reaction path, and kinetics of CO2 methanolization. The results show that Cu-ZnO heterostructure improves the HCOO path of CO2 hydrogenation by metal-support interaction (MSI). A linear relationship between the adsorption energy of the intermediates via hydrogenation process was found with the correlation coefficient R2≈0.8. The order of the highest activation barriers for the overall reaction is Cu/ZnO-Au > Cu/ZnO-Mg > Cu/ZnO-Pd > Cu/ZnO-Pt > Cu/ZnO-Ga > Cu/ZnO > Cu/ZnO-Al. The diverse performance of various metal promoters was ranked as Al > Ga > Mg, Pt > Pd > Au. Our simulation work well corresponds the previous experimental results conducted by other scholars and will provide guidance for future design of the high-efficient catalysis for CO2 hydrogenation.
Systematic DFT calculations of model coal-pyrrole derivatives substituted by different functional groups are carried out. The N-H bond dissociation energies (N-H BDEs) and H-transfer activation ...energies (H-TAEs) of pyrrole derivatives are fully evaluated to elucidate the effect of the type of substituents and their position on the molecular reactivity. The results indicate that compounds substituted with electron-donating groups (EDGs) are more prone to pyrolysis while those substituted with electron-withdrawing groups (EWGs) are difficult to pyrolyze. Furthermore, quantitative structure–property relationship (QSPR) models for N-H BDEs and H-TAEs about pyrrole derivatives are built with multiple linear regression (MLR) and support vector machine (SVM). The final results show that the SVM-QSPR model has better fitness, prediction, and robustness, while the MLR-QSPR model can express the physical meaning better. The effects of functional groups on pyrolysis are clarified by the models presented in this paper, which will support the optimization of ultra-low NO x combustion.
Diabetic foot ulcers (DFUs) are a severe and rapidly growing diabetic complication, but treating DFUs remains a challenge for the existing therapies are expensive and highly non-responsive. Recently, ...we discovered that a natural adhesive from snail mucus can promote skin wound healing. Herein, inspired by the finding, we developed a double-network hydrogel biomaterial that composed of snail glycosaminoglycan (AFG) and methacrylated gelatin (GelMA), in which AFG is the main bioactive component of snail mucus and GelMA provides a scaffold mimicking the proteins in snail mucus. The biomimetic hydrogel exhibited strong tissue adhesion, potent anti-inflammatory activity, and excellent biocompatibility. The biodegradable AFG/GelMA hydrogel markedly promoted chronic wound healing in both STZ-induced type 1 diabetic rat and db/db mouse models after a single treatment. Further mechanistic research showed that the hydrogel significantly attenuated inflammation by sequestrating pro-inflammatory cytokines, as well as downregulated their expression by inhibiting NF-ĸB signaling pathway, and it can also promote macrophage polarization to M2 phenotype. Taken together, the bioinspired hydrogel can effectively promote the transition of chronic wounds from inflammation to proliferation stage. These data suggest that the AFG/GelMA hydrogel is a promising therapeutic biomaterial for the treatment of chronic diabetic wounds.
Soluble irons from aerosol particles play a key role in assessment of biological and toxicological effects as a result of their oxidative potential. Several factors are responsible for controlling ...the solubility of irons released from aerosol mineral particles in acid solution. Here, factors of H+ concentrations, temperatures, solid–liquid ratios and particle sizes on acidsoluble irons from chlorite mineral particles were investigated in 48h. Our data demonstrated that the higher acidity, higher temperature, lower solid–liquid ratio, and smaller particle size were factors that positively influenced the solubility of FeT and Fe(II), and that the higher acidity, lower temperature, higher solid–liquid ratio, and smaller particle size positively improved the solubility of Fe(III). Fe(II) dissolved more easily than Fe(III) in the acid solution under all conditions; the percentage of Fe(III) among the total iron released from chlorite particles could be promoted by increasing the acidity, solid–liquid ratio, and particle size, or by lowering the temperature. In this acidolysis process, there is a clear order of importance of these influencing factors, H+ concentrations > particle sizes > temperatures and solid–liquid ratios. In addition, the dissolution data for FeT at pH = 0.7 and pH = 1 in 48 h could be well described and predicted by non-linear fitting with r2 > 0.99 according to continuous dissolution model. And the analysis of oxidative potential showed that Fe(II) dissolved from chlorite possessed a dominant position in generating reactive oxygen species.
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•Acidity, temperature, solid-liquid ratio and particle size positively affected the solubility of FeT and Fe(II).•Fe(II) dissolved more easily than Fe(III) in acid solution.•Fe(II) displayed a dominant position in generating reactive oxygen species.
CeOsub.2 is widely used in the field of chemical–mechanical polishing for integrated circuits. Morphology, particle size, crystallinity, and Cesup.3+ concentration are crucial factors that affect ...polishing performance. In this study, we successfully synthesized two novel triangular CeOsub.2 abrasives with similar particle sizes (600 nm) but different morphologies and Cesup.3+ concentrations using a microwave-assisted hydrothermal method with high-concentration raw materials, and no surfactants or template agents were added. It is generally believed that CeOsub.2 with a higher Cesup.3+ concentration leads to better polishing performance. However, the results of polishing indicate that CeOsub.2 synthesized at 200 °C, despite its lower Cesup.3+ concentration, demonstrates outstanding polishing performance, achieving a polishing rate of 324 nm/min, and the Ssub.a of Si wafers decreased by 3.6% after polishing. This suggests that, under similar particle size conditions, the morphology of CeOsub.2 plays a dominant role in the mechanical effects during the polishing process. Additionally, compared to commercial polishing slurries, the synthesized samples demonstrated better polishing performance. This indicates that, in CMP, the pursuit of smaller spherical abrasives may not be necessary. Instead, the appropriate shape and particle size can better balance the material removal rate and surface roughness.
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